Classification - Fundamentals & Practical Applications

Overview

Classification problems are a frequent challenge in data science. This course empowers you to understand and apply key algorithms to predict and enhance business decision-making.

Suited for those aspiring to be data scientists or with a focus on analytics and business intelligence, this course provides a deep dive into classification problems, solutions, and critical interpretations.

Learn from fundamental techniques like Logistic Regression, KNN, and SVM models, and gain skills in implementing these techniques using Excel and Python. Discover how to create loops for parallel model execution, and delve into model evaluation with a dedicated chapter on interpreting outputs using metrics and the confusion matrix, considering business implications of false negatives and positives.

Explore advanced techniques including feature importance, SHAP values, and PDP plots.

By course completion, you will:

• Differentiate between classic classification techniques, their assumptions, and practical applications.
• Execute logistic regression in Excel and RegressIt.
• Build basic classification models in Python using statsmodels and sklearn.
• Evaluate and interpret classification model performance.

Designed for data enthusiasts, this course introduces you to key terms, enabling you to engage in data science discussions, perform analysis, and comprehend its business benefits.

University:

Coursera

Categories:

Python Courses, Machine Learning Courses, Business Intelligence Courses, Data Science Courses, Model Evaluation Courses, Classification Courses, Logistic Regression Courses, K-Nearest Neighbors Courses

Subjects